Main Effects

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = tightness ~ cond, data = mdp_study3clean)
## 
## $cond
##                         diff        lwr       upr     p adj
## relative-different 0.3597222 -0.1883237 0.9077682 0.2693043
## same-different     0.7104167  0.1692647 1.2515687 0.0063218
## same-relative      0.3506944 -0.1317728 0.8331617 0.2010361

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = n_vio ~ cond, data = mdp_study3clean)
## 
## $cond
##                          diff       lwr        upr     p adj
## relative-different -0.4472222 -1.206162  0.3117179 0.3464458
## same-different     -0.8781250 -1.627518 -0.1287317 0.0170485
## same-relative      -0.4309028 -1.099029  0.2372232 0.2816249

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = punish ~ cond, data = mdp_study3clean)
## 
## $cond
##                         diff          lwr       upr     p adj
## relative-different 0.2145833 -0.181452404 0.6106191 0.4075246
## same-different     0.5726563  0.181602293 0.9637102 0.0019613
## same-relative      0.3580729  0.009426482 0.7067194 0.0426336

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = ocb ~ cond, data = mdp_study3clean)
## 
## $cond
##                         diff        lwr       upr     p adj
## relative-different 0.0031250 -0.4089027 0.4151527 0.9998225
## same-different     0.1441406 -0.2627041 0.5509854 0.6799319
## same-relative      0.1410156 -0.2217092 0.5037404 0.6286791

Controls

## $`as dv: Tightness`
## 
## Call:
## lm(formula = y ~ cond_label + gender + Age + Race + feeling_crt_1, 
##     data = mdp_study3clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0056 -0.5954  0.0116  0.7746  2.9796 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         4.356914   0.421713  10.331  < 2e-16 ***
## cond_labelRelative  0.420905   0.243281   1.730  0.08569 .  
## cond_labelSame      0.742961   0.236453   3.142  0.00203 ** 
## gender             -0.088482   0.193882  -0.456  0.64879    
## Age                -0.005361   0.007303  -0.734  0.46407    
## Race1,2            -0.296122   1.150539  -0.257  0.79725    
## Race1,2,5           1.182922   1.182477   1.000  0.31876    
## Race1,3,5          -0.252678   1.151140  -0.220  0.82656    
## Race1,7             0.627835   0.670767   0.936  0.35080    
## Race2               0.229530   0.327779   0.700  0.48487    
## Race2,5             1.784770   1.160594   1.538  0.12623    
## Race3              -0.068580   0.320908  -0.214  0.83107    
## Race4               0.474411   0.823699   0.576  0.56552    
## Race5               1.078001   1.190182   0.906  0.36654    
## Race7               1.368807   0.675812   2.025  0.04462 *  
## feeling_crt_1       0.035459   0.059385   0.597  0.55135    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.135 on 148 degrees of freedom
## Multiple R-squared:  0.1333, Adjusted R-squared:  0.04547 
## F-statistic: 1.518 on 15 and 148 DF,  p-value: 0.1056
## 
## 
## $`as dv: NormVio.\nAccept.`
## 
## Call:
## lm(formula = y ~ cond_label + gender + Age + Race + feeling_crt_1, 
##     data = mdp_study3clean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4581 -0.8925 -0.1696  0.7463  3.7974 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         4.447715   0.504635   8.814 3.04e-15 ***
## cond_labelRelative -0.285837   0.291118  -0.982  0.32777    
## cond_labelSame     -0.928934   0.282947  -3.283  0.00128 ** 
## gender              0.214168   0.232005   0.923  0.35745    
## Age                -0.015474   0.008739  -1.771  0.07867 .  
## Race1,2             1.304697   1.376772   0.948  0.34485    
## Race1,2,5           2.331534   1.414990   1.648  0.10153    
## Race1,3,5           0.040639   1.377491   0.030  0.97650    
## Race1,7             0.031137   0.802661   0.039  0.96911    
## Race2               0.248739   0.392231   0.634  0.52695    
## Race2,5            -1.942889   1.388805  -1.399  0.16392    
## Race3               0.289400   0.384009   0.754  0.45227    
## Race4              -1.257838   0.985665  -1.276  0.20391    
## Race5               0.563411   1.424210   0.396  0.69297    
## Race7               1.345059   0.808699   1.663  0.09838 .  
## feeling_crt_1       0.440790   0.071062   6.203 5.27e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.358 on 148 degrees of freedom
## Multiple R-squared:  0.3452, Adjusted R-squared:  0.2788 
## F-statistic: 5.201 on 15 and 148 DF,  p-value: 3.039e-08
## 
## 
## $`as dv: Punish`
## 
## Call:
## lm(formula = y ~ cond_label + gender + Age + Race + feeling_crt_1, 
##     data = mdp_study3clean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.96106 -0.53249  0.01807  0.52917  1.77667 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         3.145937   0.301961  10.418  < 2e-16 ***
## cond_labelRelative  0.218415   0.174197   1.254  0.21188    
## cond_labelSame      0.564076   0.169308   3.332  0.00109 ** 
## gender             -0.217945   0.138826  -1.570  0.11857    
## Age                 0.007826   0.005229   1.497  0.13663    
## Race1,2            -0.226430   0.823825  -0.275  0.78381    
## Race1,2,5           0.820685   0.846694   0.969  0.33399    
## Race1,3,5          -0.369048   0.824255  -0.448  0.65500    
## Race1,7             0.825019   0.480292   1.718  0.08793 .  
## Race2               0.093733   0.234701   0.399  0.69019    
## Race2,5            -0.101213   0.831025  -0.122  0.90323    
## Race3              -0.120943   0.229781  -0.526  0.59944    
## Race4               0.489469   0.589797   0.830  0.40794    
## Race5              -1.268059   0.852211  -1.488  0.13889    
## Race7              -0.552563   0.483905  -1.142  0.25535    
## feeling_crt_1      -0.002801   0.042522  -0.066  0.94756    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8128 on 148 degrees of freedom
## Multiple R-squared:  0.165,  Adjusted R-squared:  0.08034 
## F-statistic: 1.949 on 15 and 148 DF,  p-value: 0.02268
## 
## 
## $`as dv: OCBs`
## 
## Call:
## lm(formula = y ~ cond_label + gender + Age + Race + feeling_crt_1, 
##     data = mdp_study3clean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.77622 -0.53847 -0.05765  0.49999  2.23741 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         2.940949   0.288575  10.191  < 2e-16 ***
## cond_labelRelative  0.098290   0.166476   0.590  0.55581    
## cond_labelSame      0.110286   0.161803   0.682  0.49656    
## gender              0.024869   0.132672   0.187  0.85157    
## Age                -0.009087   0.004997  -1.818  0.07103 .  
## Race1,2             0.116498   0.787307   0.148  0.88257    
## Race1,2,5           0.656831   0.809162   0.812  0.41824    
## Race1,3,5           0.909026   0.787718   1.154  0.25036    
## Race1,7            -0.233162   0.459002  -0.508  0.61223    
## Race2               0.306206   0.224297   1.365  0.17427    
## Race2,5            -1.403898   0.794188  -1.768  0.07917 .  
## Race3               0.043427   0.219596   0.198  0.84351    
## Race4              -1.138397   0.563652  -2.020  0.04522 *  
## Race5               0.479674   0.814434   0.589  0.55678    
## Race7               1.218926   0.462454   2.636  0.00929 ** 
## feeling_crt_1       0.164749   0.040637   4.054  8.1e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7768 on 148 degrees of freedom
## Multiple R-squared:  0.2433, Adjusted R-squared:  0.1666 
## F-statistic: 3.172 on 15 and 148 DF,  p-value: 0.0001567

Mediation

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = n_vio ~ cond_num + (tightness), data = mdp_study3clean)
## 
## The DV (Y) was  n_vio . The IV (X) was  cond_num . The mediating variable(s) =  tightness .
## 
## Total effect(c) of  cond_num  on  n_vio  =  -0.44   S.E. =  0.16  t  =  -2.8  df=  162   with p =  0.0057
## Direct effect (c') of  cond_num  on  n_vio  removing  tightness  =  -0.49   S.E. =  0.16  t  =  -3.07  df=  161   with p =  0.0025
## Indirect effect (ab) of  cond_num  on  n_vio  through  tightness   =  0.05 
## Mean bootstrapped indirect effect =  0.06  with standard error =  0.05  Lower CI =  -0.02    Upper CI =  0.16
## R = 0.24 R2 = 0.06   F = 4.97 on 2 and 161 DF   p-value:  0.00252 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary

## 
## Mediation/Moderation Analysis 
## Call: psych::mediate(y = punish ~ cond_num + (tightness), data = mdp_study3clean)
## 
## The DV (Y) was  punish . The IV (X) was  cond_num . The mediating variable(s) =  tightness .
## 
## Total effect(c) of  cond_num  on  punish  =  0.29   S.E. =  0.08  t  =  3.58  df=  162   with p =  0.00045
## Direct effect (c') of  cond_num  on  punish  removing  tightness  =  0.26   S.E. =  0.08  t  =  3.09  df=  161   with p =  0.0023
## Indirect effect (ab) of  cond_num  on  punish  through  tightness   =  0.03 
## Mean bootstrapped indirect effect =  0.03  with standard error =  0.03  Lower CI =  -0.01    Upper CI =  0.1
## R = 0.3 R2 = 0.09   F = 7.93 on 2 and 161 DF   p-value:  5.76e-05 
## 
##  To see the longer output, specify short = FALSE in the print statement or ask for the summary